Asymmetric Encoder-Decoder Speech Separation for Lower Complexity
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Solution Overview
Problem
Existing speech extraction systems require complex computations to separate speech from multiple speakers in a single space, necessitating a reduction in system complexity while maintaining performance.
Innovation Solution
A speech separation device employing an asymmetric encoder-decoder architecture with a separation encoder, speaker separation unit, and reconstruction decoder, utilizing global-local transformers and Siamese transformers to downsample and upsample feature sequences for each speaker, along with a loss calculation unit to optimize parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a speech extraction system uses traditional methods to separate speech from multiple speakers, then speech separation can be achieved, but system complexity increases significantly
Solution Approach 1:
The system segments the speech separation task into distinct encoding and decoding stages with intermediate feature processing. The encoder divides the input speech signal into multiple feature sequences at different resolution levels, which are then processed separately before being combined in the decoder to reconstruct separated speech signals for each speaker.
Solution Approach 2:
The system transforms the speech signal from time-domain representation into a multi-dimensional feature space using Fourier transforms and complex-valued operations. This dimensional transformation enables more effective separation by exploiting frequency-domain characteristics and phase information that are not apparent in the original time-domain signal.
2Measurement precision
If the encoder uses multiple encoding stages with global-local transformers, then feature extraction quality improves, but computational complexity increases
Solution Approach 1:
The encoding process is segmented into multiple stages, each handling different aspects of feature extraction. Global-local transformers are applied at specific stages to capture both overall speech characteristics and local speaker-specific features, allowing progressive refinement of the encoded representations without requiring all components to operate simultaneously at full complexity.
Solution Approach 2:
Different parts of the encoding process use different transformation approaches. Global transformers process entire feature sequences to capture overall patterns, while local transformers process specific regions or time frames to capture localized speaker characteristics. This localized application of complexity ensures computational resources are focused where most needed.
3Manufacturing precision
If the system processes all feature sequences through multiple decoding stages, then reconstruction accuracy improves, but processing time increases
Solution Approach 1:
The encoder performs preliminary processing to organize and structure the speech features into multiple sequences with different resolutions and characteristics before decoding begins. This preliminary organization allows the decoder to more efficiently reconstruct separated speech by leveraging the pre-processed structure rather than having to extract all features during the decoding phase.
Solution Approach 2:
Multiple decoding stages process different feature sequences in a continuous pipeline, with each stage contributing to the final reconstruction. Rather than completing one full decoding sequence before starting another, the system maintains continuous processing across parallel feature sequences, improving overall throughput while maintaining accuracy.
Data Source
AI summary
A speech separation device according to an embodiment of the present disclosure may include a separation encoder, a speaker separation unit, and a reconstruction decoder. The separation encoder may provide an encoded feature sequence by downsampling an input representation generated based on a speech signal. The speaker separation unit may provide a plurality of separated feature sequences by separating the encoded feature sequence for each of a plurality of speakers included in the speech signal. The reconstruction decoder may provide an output representation for each speaker by upsampling the separated feature sequence.The speech separation device according to the present disclosure may not only improve system performance more effectively but also reduce system complexity by providing the plurality of separated feature sequences, each separated for the plurality of speakers, by using the speaker separation unit disposed between the encoder and the decoder.